Human-machine Collaboration Research Articles

Human–machine collaboration: exploring professional identity threat within the records and information management community

Purpose This study explores how records and information management (RIM) practitioners perceive threats from artificial intelligence (AI) to their professional identity, and how these perceptions impact their willingness to collaborate with AI-based systems.Design/methodology/approach The research utilized the “AI identity threat framework” to analyse quantitative data from 404 respondents and qualitative data from 21 participants in six Eastern and Southern African countries. Data were analysed using the Structural Equation Modelling technique on IMB-SPSS-AMOS software.Findings AI identity, loss of skills/expertise, changes of work and loss of autonomy significantly predicted professional identity threat (PIT). PIT was found to negatively and significantly predict the intention to use AI-based systems. The proposed moderating variables had no interaction effect. Interviewees affirmed AI as a collaborator, temporal distance, fear of job loss, need for upskilling, shifting roles as predictors of use intention and PIT.Research limitations/implications This study successfully affirmed the existing AI identity threat framework by demonstrating its effectiveness in RIM, identifying new threats and highlighting the varying impact of AI threats across contexts.Originality/value The study findings contribute to the understudied area of AI behavioural use intention within the RIM field and African context.

Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments

Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in applications such as health monitoring, post-disaster rescue, and intelligent control. However, conventional microphone-based systems remain challenging for complex human-machine collaboration in noisy environments. Herein, an anti-noise triboelectric acoustic sensor (Anti-noise TEAS) based on flexible nanopillar structures is developed and integrated with a convolutional neural network-based deep learning model (Anti-noise TEAS-DLM). This highly synergistic system enables robust acoustic signal recognition for human-machine collaboration in complex, noisy scenarios. The Anti-noise TEAS directly captures acoustic fundamental frequency signals from laryngeal mixed-mode vibrations through contact sensing, while effectively suppressing environmental noise by optimizing device-structure buffering. The acoustic signals are subsequently processed and semantically decoded by the DLM, ensuring high-fidelity interpretation. Evaluated in both simulated virtual and real-life noisy environments, the Anti-noise TEAS-DLM demonstrates near-perfect noise immunity and reliably transmits various voice commands to guide robotic systems in executing complex post-disaster rescue tasks with high precision. The combined anti-noise robustness and execution accuracy endow this DLM-enhanced Anti-noise TEAS as a highly promising platform for next-generation human-machine collaborative systems operating in challenging noisy environments.

Industry 5.0 and Human-Centered Energy System: A Comprehensive Review with Socio-Economic Viewpoints

Industry 5.0 transforms industrial ecosystems via artificial intelligence (AI), human–machine collaboration, and sustainability-focused innovations. This systematic literature review examines Industry 5.0′s role in energy transition through digital transformation, sustainable supply chains, and energy efficiency strategies. Key findings highlight AI-driven smart grids, blockchain-enabled energy transactions, and digital twin simulations as enablers of low-carbon, adaptive industrial operations. This review uniquely integrates technological, managerial, and policy perspectives, providing actionable insights for policymakers and industry leaders. Industry 5.0 enhances innovative energy management, renewable energy integration, and flexible energy distribution, strengthening resilience and sustainability. It fosters environmental responsibility, social impact, and circular economy principles, laying the foundation for a low-carbon economy and accelerating the global energy transition.

Research on the Reconstruction and Communication Effect of Generative Artificial Intelligence on Communication Content: A Semiotic Perspective

Generative artificial intelligence, as an important transformative force in contemporary communication technology, is profoundly changing the generation mechanism of communication content and the implementation path of communication effects. From a semiotic perspective, this article explores how AI technology can reconstruct the symbolic construction logic, content form, and discourse power structure in communication content, and further analyzes its impact on audience perception, information reception, and feedback mechanisms, communication controllability, and uncertainty at the level of communication effectiveness. Research has found that the deep intervention of AI has led to structured, programmatic, and mimetic dissemination of content. The decoding path of the audience is becoming increasingly complex, and the feedback is more immediate but lacks depth. At the same time, it also brings potential risks such as unclear ethical responsibilities and decreased information authenticity. At the end of the article, a thinking path for future communication research and practice is proposed from four dimensions: ethical regulation, platform responsibility, human-machine collaboration, and disciplinary integration, aiming to provide a theoretical reference for building a more rational, standardized, and human-oriented AI communication environment.

Research on Hybrid Collaborative Development Model Based on Multi-Dimensional Behavioral Information

This paper aims to propose a hybrid collaborative development model based on multi-dimensional behavioral information (HCDMB) to deal with systemic problems in modern software engineering, such as the low efficiency of cross-stage collaboration, the fragmentation of the intelligent tool chain, and the imperfect human–machine collaboration mechanism. This paper focuses on the stages of requirements analysis, software development, software testing and software operation and maintenance in the process of software development. By integrating the multi-dimensional characteristics of the development behavior track, collaboration interaction record and product application data in the process of project promotion, the mixture of experts (MoE) model is introduced to break through the rigid constraints of the traditional tool chain. Reinforcement learning combined with human feedback is used to optimize the MoE dynamic routing mechanism. At the same time, the few-shot context learning method is used to build different expert models, which further improve the reasoning efficiency and knowledge transfer ability of the system in different scenarios. The HCDMB model proposed in this paper can be viewed as an important breakthrough in the software engineering collaboration paradigm, so as to provide innovative solutions to the many problems faced by dynamic requirements and diverse scenarios based on artificial intelligence technology in the field of software engineering involving different project personnel.

Navigating the transition to industry 5.0: advancing sustainability, resilience, and human-centricity in Spanish supply chain management

This study examines the adoption of Industry 5.0 (I5.0) principles within Supply Chain Management (SCM), focusing on the Spanish context as an early adopter. While Industry 4.0 (I4.0) prioritised automation and efficiency, I5.0 introduces a paradigm shift by integrating sustainability, resilience, and human-centricity into supply chains. Using a qualitative methodology, this study analyses 25 interviews with industry professionals, applying a co-occurrence analysis to uncover interdependencies between organisational culture, leadership, and digital tools in the transition to I5.0. The findings reveal that resistance to change, financial constraints, and sectoral misalignment are critical barriers, particularly for small and medium enterprises. However, proactive leadership, the integration of circular economy practices, and customised digitalisation strategies emerge as key enablers. A significant contribution of this research is its identification of human–machine collaboration and digitalisation as catalysts for both sustainability and resilience in SCM, demonstrating how these elements enhance adaptability and long-term competitiveness. The study moves beyond theoretical discussions to provide practical strategies for overcoming transition barriers, emphasising the role of leadership-driven cultural transformation, workforce engagement, and sector-specific digitalisation approaches. By embedding sustainability and human-centric digital innovation into SCM, organisations can create agile, resilient, and people-oriented supply chains that align with the evolving demands of I5.0. This research contributes new empirical insights into I5.0 adoption, offering both theoretical advancements and actionable implications for practitioners.

Analysis of the Impact of Portfolio Optimization Algorithms on Fund Performance

This study explores the impact mechanism of portfolio optimization algorithms on fund performance. Based on theoretical analysis and empirical research, it is found that optimization algorithms significantly affect fund performance in three paths: return enhancement, risk control and cost management, but the effects are moderated by the market environment and algorithm type. Machine learning and hybrid optimization strategies exhibit better risk-adjusted returns, while traditional algorithms maintain value in specific market environments. The study reveals that optimization algorithms face three major challenges: model risk, market contrariness, and regulatory compliance, and proposes coping strategies based on multi-scenario testing and human-machine collaboration to provide theoretical guidance for the practical application of optimization algorithms.

Research on Financial Investment Strategy Optimization with The Aid of Large Language Model

This paper discusses the role and influence of large language model (LLM) in the optimization of financial investment strategy from the perspective of humanities and social sciences. By reviewing the evolution of investment behavior, psychology, and LLM, the paper builds a theoretical framework for understanding how these advanced technologies can help investors make more informed decisions. In particular, this paper emphasizes the importance of LLM as an information intermediary, which can interpret and integrate information from financial news, social media and other channels to help investors better grasp market sentiment and facilitate exchanges and interactions among investors. Further, the paper analyzes the ethical principles and social responsibilities that should be considered when applying LLM to investments, and proposes a new investment paradigm of human-machine collaboration, in which human intelligence and technical capabilities complement each other. Finally, the paper looks forward to the impact of technological progress and social change on financial markets, and discusses the challenges and opportunities brought about by the integration of different cultures and technologies in the context of globalization. This study not only provides policy makers with recommendations on the responsible application of technology, but also points the way for future research aimed at promoting a more transparent, fair and efficient financial environment.

A study on supply chain 5.0 research: A visual analysis by bibliometrix R-tool

Studies on recent industrial developments and their impacts are one of the majorities of the current literature, and it is a rapidly growing area. With this view, this paper presents a comprehensive bibliometric analysis of research on Supply Chain 5.0 (SC 5.0), an emerging paradigm that integrates Industry 5.0 principles with supply chain management to enhance human-machine collaboration, sustainability, and resilience. Utilizing data from prominent academic databases, this study examines the evolution of SC 5.0 research and identifies key themes. Bibliometrix software, which uses R language and is therefore known as R-tool, is utilized for analysis. It employs various bibliometric techniques, including co-occurrence analysis, keyword co-occurrence, and thematic mapping, to uncover the intellectual structure and trends within the field. Findings reveal a rapid publication growth, emphasizing technological advancements, sustainability, and human-centric approaches. At the end of the study, future research ideas are presented under the themes of supply chain resilience and agility, human-centric approaches in supply chains, impacts of SC 5.0 in emerging economies, and sustainable SC 5.0 concepts. This paper contributes to the academic discourse by providing a detailed overview of the current state of SC 5.0 research, highlighting gaps, and suggesting future research directions to advance the field.

Is control necessary for drivers? Exploring the influence of human-machine collaboration modes on driving behavior and subjective perception under different hazard visibility scenarios.

Is control necessary for drivers? Exploring the influence of human-machine collaboration modes on driving behavior and subjective perception under different hazard visibility scenarios.

Intelligent Systems for Inorganic Nanomaterial Synthesis.

Inorganic nanomaterials are pivotal foundational materials driving traditional industries' transformation and emerging sectors' evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, and complex quality control requirements. This review systematically examines strategies for constructing automated synthesis systems to enhance the production efficiency of inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, and artificial intelligence (AI)-enabled intelligent process control are analyzed. Case studies on quantum dots and gold nanoparticles demonstrate the enhanced efficiency of closed-loop synthesis systems and their machine learning-enabled autonomous optimization of process parameters. The study highlights the critical role of automation, intelligent technologies, and human-machine collaboration in elucidating synthesis mechanisms. Current challenges in cross-scale mechanistic modeling, high-throughput experimental integration, and standardized database development are discussed. Finally, the prospects of AI-driven synthesis systems are envisioned, emphasizing their potential to accelerate novel material discovery and revolutionize nanomanufacturing paradigms within the framework of AI-plus initiatives.

The Integration of Artificial Intelligence and Social Work: Opportunities, Risks, and Future Directions

With the rapid development of artificial intelligence (AI) technology, its potential applications in the field of social work are becoming increasingly evident. This paper explores the opportunities, risks, and future directions of the integration of artificial intelligence and social work. Through automated tools, data analysis, and intelligent support systems, AI has significantly enhanced service efficiency, precision, and coverage, while also promoting interdisciplinary collaboration and innovation. However, its application also faces risks such as ethical and privacy concerns, the loss of humanistic care due to over-reliance on technology, uneven resource distribution, and policy lag. To address these challenges, future efforts should focus on strengthening ethical guidelines and policy frameworks, improving the technical literacy of social workers, promoting equitable access to technology, exploring human-machine collaboration models, and enhancing interdisciplinary research. This paper aims to provide theoretical support and practical insights for social work practitioners, policymakers, and technology developers, fostering the deep integration of artificial intelligence and social work to build a more intelligent and human-centered social service system.

Research on the Application of AI in Hotel Management under Digital Transformation: A Case Study of Intelligent Customer Service and Customer Experience Optimization

With the rapid development of The digital transformation in hotel industry, this field is undergoing rapid digitization driven by AI, and big data. The integration of artificial intelligence (AI) into hotel management has become a cornerstone of digital transformation, reshaping operational efficiency, customer satisfaction, and labor costs. This paper explores the practical applications of AI technologies—such as chat-bots, intelligent recommendation systems, and voice-controlled assistants—in hotel booking, customer service, and personalized experiences. Through case studies and empirical analysis, it evaluates the challenges of AI implementation, including data privacy concerns, private information disclosure and technological adaptability, while these proposing solutions such as human-machine collaboration and phased digital strategies. The study incorporates comparative analyses of AI adoption across geographies, ethical debates on automation, and predictive models for future AI-driven hospitality ecosystems. The findings highlight that AI-driven innovations can enhance service quality but require balanced integration with human centre practices to achieve sustainable growth in the hospitality industry.

Irrationality-Aware Human Machine Collaboration: Mitigating Alterfactual Irrationality in Copy Trading

Artificial intelligence (AI) algorithms are trained on human-generated data, but what if that data reflects irrational human decision making? To tackle this challenge, Shen et al. developed a new irrationality-aware human-machine collaboration (IA-HMC) framework, designed to help AI recognize and adapt to human irrationality. A key concept introduced in this framework is “alterfactual irrationality”—a term used to describe human decisions influenced by irrelevant alternatives. The researchers applied this idea to copy trading, a popular investment strategy where everyday investors (followers) mimic the trades of expert traders. They identified two major irrational behaviors affecting followers: herding behavior—blindly following others without independent analysis; and identity bias—making investment choices based on who made the trade rather than its actual merit. By developing irrationality-aware machine learning methods, the study showed that AI can help followers make better trading decisions. Their approach led to a 49% improvement in success rates compared to human decisions alone and a 10.2% improvement over previous AI-driven methods. This research presents an innovative approach in human-AI collaboration, showing that for AI to truly align with human needs, it must first learn to account for and correct human irrationality.

The Evolution of Human-AI Collaboration in Cloud Technology: Transforming Industrial Operations

The integration of Artificial Intelligence with cloud technology has revolutionized human-machine collaboration across industrial sectors, transforming traditional workflows and decision-making processes. This technological convergence has particularly impacted healthcare delivery, customer relationship management, and inventory control systems. Organizations implementing AI-cloud solutions have experienced enhanced operational efficiency, improved data processing capabilities, and more sophisticated approaches to resource management. The synergy between human expertise and AI capabilities has created a balanced framework where automated systems complement human judgment, leading to more nuanced decision-making and strategic planning. Implementation strategies focusing on comprehensive change management, workforce development, and security considerations have proven crucial for successful integration. The evolution of these technologies continues to shape the future of industrial operations, promising further advancements in personalization, analytics, and operational excellence.

Consequence-Aware Takeovers: Enhancing Safety in Autonomous Driving Transitions

Awareness of behavioral consequences significantly influences focus, priorities, and emotions. This study investigates the impact of informing drivers about potential takeover consequences on human-machine collaboration in conditional autonomous driving. We recruited 32 licensed drivers and randomly assigned them to groups with and without informed consequences. Each group completed 8 distinct takeover tasks, each with varying consequences of not taking over. We assessed takeover performance, subjective evaluations (situational awareness, workload), and physiological stress responses (electrocardiogram, electromyogram) to provide a comprehensive evaluation of takeover safety. Our findings indicate that drivers informed of the consequences demonstrated superior takeover performance, evidenced by increased time-to-collision, reduced maximum lateral acceleration, and decreased trajectory deviation. Additionally, disclosing consequences increased drivers’ perceived attentional demands, elevating workload while maintaining stable stress levels during takeover. Future research should explore how to balance workload with driving performance when informing drivers of the consequences of not taking over.

Thermal environment characteristics and factory landscape design of precision manufacturing process based on human–machine collaboration

Thermal environment characteristics and factory landscape design of precision manufacturing process based on human–machine collaboration

Intuitive interaction flow: A dual-loop human‒machine collaboration task allocation model and an experimental study

Intuitive interaction flow: A dual-loop human‒machine collaboration task allocation model and an experimental study

Driver's trust assessment based on situational awareness under human-machine collaboration driving

Driver's trust assessment based on situational awareness under human-machine collaboration driving

The Impact of Industrial Revolution 4.0 and 5.0 on the Banking Industry

Introduction: The Fourth and Fifth Industrial Revolutions (IR 4.0 and IR 5.0) have significantly influenced the digital transformation of the banking industry. While IR 4.0 has spurred the adoption of automation, artificial intelligence (AI), and data-driven decision-making, IR 5.0 introduces a more human-centric vision—one that prioritizes ethics, sustainability, and inclusive innovation. However, the extent to which these paradigms have shaped scholarly discourse remains underexplored. Objectives: This study aims to map the intellectual and thematic evolution of banking research in the contexts of IR 4.0 and IR 5.0. It investigates dominant topics, influential contributors, and emerging trends, while identifying conceptual gaps that point toward future research opportunities, particularly those centered on trust, human-machine collaboration, and sustainable finance. Methods: A bibliometric analysis was conducted on 19,192 articles related to banking, 9,472 articles on IR 4.0, and 393 articles on IR 5.0 indexed in Scopus from 2011 to 2023. Using VOSviewer and Biblioshiny, the study performed co-occurrence network analysis, thematic evolution tracking, and cluster mapping to assess scientific performance and visualize conceptual structures. Results: IR 4.0 has become a dominant research theme in banking, with a high lexical overlap and thematic concentration on digital banking, AI, blockchain, and automation. These technologies have transformed banking operations and customer engagement models. Conversely, IR 5.0 remains early but introduces transformative values such as ethics, personalization, and sustainability. Thematic evolution gradually shifts from risk mitigation and regulatory compliance toward inclusive innovation and ethical AI. Strategic maps highlight the rise of niche and emerging themes, including CSR, ESG, and digital trust, signaling a paradigmatic transition toward human-centered banking. Conclusions: Integrating IR 5.0 values—ethical governance, human-machine collaboration, and environmental responsibility—represents a critical next step for scholars and practitioners. While IR 4.0 has laid the technical foundation, IR 5.0 provides a framework for embedding purpose and trust into digital transformation. Future research should focus on operationalizing IR 5.0 within banking systems, bridging the efficiency of automation with the imperatives of equity and sustainability.